Computer systems have evolved enormously in the past 60 years. Initial computer systems were room-sized, vacuum-tube-based behemoths with far less computational bandwidth and smaller data-storage capacities than a modern smart phone or even a microprocessor controller embedded in any of various consumer appliances and devices. Initial computer systems ran primitive programs one at a time, without the benefit of operating systems, high-level languages, and networking. Over time, parallel development of hardware, compilers, operating systems, virtualization technologies, and distributed-computing technologies has led to modern distributed computing systems, including cloud-computing facilities, that feature hundreds, thousands, tens of thousands, or more high-end servers, each including multiple multi-core processors, that can access remote computer systems and that can be accessed by remote client computers throughout the world through sophisticated electronic communications. As the complexity of computer systems has grown, the administration and management of computer systems has exponentially grown in complexity, in the volume of data generated and stored for administration and management purposes, and in the computational-bandwidth used for collecting and processing data that reflects the internal operational state of the computer systems and their subsystems and components. While the operational state of an early computer system may well have been encapsulated in a handful of status registers and a modest amount of information printed from teletype consoles, gigabytes or terabytes of metric data may be generated and stored by internal automated monitoring, administration, and management subsystems within a modern distributed computing system on a daily or weekly basis. Storage and processing of these large volumes of data generated by automated monitoring, administration, and maintenance subsystems within distributed computing systems is rapidly becoming a computational bottleneck with respect to further evolution, expansion, and improvement of distributed computing systems. For this reason, designers, developers, vendors, and, ultimately, users of computer systems continue to seek methods and subsystems to more efficiently store, process, and interpret the voluminous amount of metric data internally generated within distributed computing systems to facilitate automated administration and management of distributed computing systems, including diagnosing performance and operational problems, anticipating such problems, and automatically reconfiguring and repairing distributed-system-components to address identified and anticipated problems.